1. Technical Field
The present teaching relates to methods and systems for advertising and/or recommendations. Specifically, the present teaching relates to methods and systems for targeted advertising, conversation measurement, and/or recommendations of targeted television programs.
2. Discussion of Technical Background
The rapid development of digital content access platforms, such as the Internet, mobile Internet, and smart TV, has made it possible for a user to electronically access virtually any content at any time from any location using any device. Such free access to digital content without limitations in time, space, or platforms has enabled great opportunity for advertisers and publishers in advertising. On the other hand, with the explosion of information, it has become increasingly important to provide users with advertisement that is relevant to the user.
Efforts have been made to attempt to deliver advertisements to targeted users who are most likely interested in the advertisements. A shortcoming of the traditional approaches is that it merely aggregates user activities on a particular platform while a user's everyday life spans across multiple platforms. For example, users' explicit interests (e.g., user's preferences declared in social networks) or implicit interests (e.g., interests inferred by analyzing the user's online content consumption) have been collected online and used as a basis for targeted advertising by known approaches. However, online behaviors constitute only a portion of a user's daily activities, which, sometimes, are insufficient to build a comprehensive and accurate user profile for the purpose of targeted advertising. This is particularly true for certain users, who are not used to using the Internet, such as elderly people. Even on the same platform, e.g., online platform, a user's activities also span cross different devices, which makes the traditional approaches even more ineffective in capturing the user's online behaviors to build a comprehensive and accurate user profile. For example, traditional approaches rely primarily on cookies in tracking users' online activities. However, these approaches are no longer suitable in today's mobile world as mobile devices usually do not have reliable cookies. As another example on the TV platform, there is currently no way to use online digital data, such as media consumption and transaction data, to create personalized TV programs to appropriate audiences.
Another line of efforts in attempting to optimize targeted advertising have been made to measure the advertisement conversion rate, which is the rate at which an advertisement exposure event leads to a corresponding advertisement conversion event. The underlying goal is to provide an indicator to the marketers, e.g., advertisers or publishers, regarding the effectiveness of their advertisements, advertisement placements, etc. The convergence of consumer devices over the past several years has created a situation where the average consumer digests media from multiple devices at different platforms (e.g., online, offline, TV, etc.) on a daily basis. For example, different activities may be performed on different devices or platforms, e.g., being exposed to an advertisement of a product on one device but making online purchase of the advertised product on another device. Sometimes, the purchase may even be made offline, e.g., at a local store. In addition, as there is a gap in time between viewing an advertisement and the actual transaction caused by the advertisement, it is even harder to link the viewing activity and purchasing activity across time. Furthermore, one user in a user group, e.g., a household, may be exposed to an advertisement but a different user from the same user group may make the purchase. These create difficulties in estimating the conversion rate of an advertisement.
Traditional approaches, however, are unable to handle the difficulties as they evaluate advertisement conversion at each platform separately to judge effectiveness or, more commonly, use a guesstimate to approximate their return on investment (ROI) on advertisement spending. For example, advertisers traditionally utilize modeling and assumptions to track the effectiveness of their campaigns, often using metrics such as click through rate (CTR) to approximate sales. However, the use of CTR or other traditionally-utilized often produce inaccurate information regarding the effectiveness of the advertising campaigns and, as a result, inhibit the ability of advertisers (or other entities) to optimize advertisement spending.